Impact of Remittances on Economic Growth and Poverty: Evidence from Pakistan
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Transcript of Impact of Remittances on Economic Growth and Poverty: Evidence from Pakistan
1
Impact of Remittances on Economic Growth and Poverty
Abdul Qayyum
Professor/ Registrar
Pakistan Institute of Development Economics, Islamabad
Muhammad Javid
Staff Economist
Pakistan Institute of Development Economics, Islamabad
and
Umaima Arif
Pakistan Institute of Development Economics, Islamabad
Abstract
The study focused on the importance of remittances inflow and its implication for economic growth and poverty reduction in Pakistan. By using ARDL approach we analyze the impact of remittances inflow on economic growth and poverty in Pakistan for the period 1973-2007. The district wise analysis of poverty suggest that overseas migration contributes to poverty alleviation in the districts of Punjab, Sindh and Balochistan however NWFP is not portraying a clear picture. The empirical evidence shows that remittances effect economic growth positively and significantly. Furthermore the study also finds that remittances have a strong and statistically significant impact on poverty reduction thus suggesting that there are substantial potential benefits associated with international migration for poor people in developing countries like Pakistan. So the importance of remittance inflows can not be denied in terms of growth enhancement and poverty reduction that consequently improves the social and economic conditions of the recipient country.
Keywords: Remittances; Growth; Poverty; Pakistan
2
1) Introduction
Remittance is an important source of foreign exchange earnings for Pakistan since 1970.
During the past four decade Pakistan received significant amount of remittances,
however, fluctuation were also observed in the inflow of remittances. Inflow of
remittances affects economic growth positively by reducing current account deficit,
improving the balance of payment position and reducing dependence on external
borrowing (Iqbal and Sttar, 2005).
Inflows of remittances increase the economic growth and reduce the poverty by
stimulating the income of the recipient country, reducing credit constraints, accelerating
investment, enhancing human development through financing better education and health
[Calaro (2008); Jongwanich (2007); Stark and Lucas (1988); Taylor (1992); Faini (2002);
Gupta et al.(2009)]. However Chami et al (2003) find that remittances have negative
impacts on economic growth of recipient country because a significant flow of
remittances reduce labor force participation and work efforts which lowers output. Thus,
the impact of remittances on economic growth and development of recipient country has
been controversial.
In case of Pakistan, a number of studies have been undertaken at micro as well as macro
level that directly or indirectly focused on the impact of remittances on growth and
development (Burney,1987; Arif, 1999; Adams,1998; Malik et al,1993; Nishat et a
,1993; Burki,1991; Kozel and Alderman,1990; Amjad,1986; Nishat and Bilgrami,1991).
The general conclusion of these studies suggest that remittances have positive effects on
economy of Pakistan in terms of aggregate consumption, investment, reduction in
current account deficit, external debt burden and improve education/skills of the
households. Furthermore, labour migration is considered to be a useful source of foreign
exchange earning (Naseem, 2004). Siddidui and Kemal (2006) explored the impact of
decline in remittances on welfare and poverty in Pakistan. The analysis shows that in
Pakistan poverty rises due to decline in remittances during nineties. Kemal (2001) finds
that remittance inflow is major variable affecting the poverty levels both through change
in income and consumption level and as well as through increase in capital stock.
3
During the current decade since the event of 9/11 the inflow of remittances in Pakistan
has increased sharply that is US$1075 millions in 2000 to US$ 6000 millions in 2007.
This massive inflow of remittances contributes in reducing current account deficit,
increasing foreign exchange reserves, stabilizing exchange rate and reducing poverty.
Generally previous studies were based on survey data and ignored the relationship
between remittances and poverty so this study contributes to existing literature by
empirically examining the impact of remittances on economic growth and poverty.
The rest of the paper is organized as follow. Section 2 presents a review of literature.
Section 3 provides an overview of overseas migration and worker’s remittances in
Pakistan. Furthermore district wise analysis of overseas migration and poverty is also
presented in this section. Model specification, data and methodology are given in section
4. Section 5 discusses empirical results, while the final section concludes the study.
2) Literature Review
An appropriate understanding of remittance and growth relationship can help policy
makers to design a suitable economic policy. Giuliano(2008) finds that remittances boost
growth in countries with less developed financial system as it provide an alternative way
to finance investment and reduce liquidity constraints. Workers remittances also play an
important role in human capital investment in the recipient country through relaxing
resource constraints. Calero (2008) explored that remittances increases school enrollment
and decrease the extent of child work. Moreover the study finds that remittances are used
to finance education when households are facing aggregate shocks as these are associated
with increased work activities. International remittances also perform an important role in
reducing the extent of inequality and poverty. Acosta et al (2007) presented the
household survey base estimates for 10 Latin American countries which confirmed that
remittances have negative though relatively small inequality and poverty reducing
effects.
Jongwanich (2007) examines the impact of workers remittances on growth and poverty in
Asia-Pacific developing countries. The empirical evidence shows remittances have a
significant impact on poverty reduction and trivial impact on growth. Burgess and
4
Vikram (2005) examine the different channels through which remittances can affect
economic activity. The study does not clearly support the short term stabilizing effect on
consumption, however the longer term economic effect of such flows seems to be
ambiguous. Catrinescu et al (2006) explored that remittances exert a weakly positive
impact on long term macroeconomic growth. Furthermore the study also supports the
idea that development impact of remittances enhances in the presence of sound
macroeconomic policies and institution.
Fayissa and Nsiah (2008) argued that remittances enhance economic growth in countries
where financial systems are not very strong by providing an alternative way to finance
investment and help to overcome liquidity constraints. Iqbal and Sattar (2005) shows that
real GDP growth is positively correlated to workers’ remittances during 1972-73 to 2002-
03 and workers’ remittances emerged to be the third important source of capital for
economic growth in Pakistan.
Adams and Page (2005) used the data of 71 developing countries in their study on
remittances, inequality, and poverty and concluded that remittances significantly reduce
the level, depth and severity of poverty in the developing world. Lucas (2005)1 argues
that remittances probably contributed in a significant way to poverty alleviation process
in case of Pakistan.
The impact of remittances on economic growth and poverty has been an extensively
discussed issue both among academics and policy makers. Although this area of research
has been explored extensively and widely, yet further research on this issue is still
required to arrive at overall judgment related to the desirability of foreign remittances for
economic growth and poverty reduction. On the basis of literature related to affects of
remittances on growth and poverty, we can summaries the following main channels
through which remittances enhance growth and ultimately reduce poverty in remittances
receiving economy.
1 Cited by Jongwanich (2007)
5
3) Overseas Migration and Workers Remittance: An Overview
Before proceeding to empirical analysis, it may be useful to have an overview of overseas
migration and development of workers remittances since 1970. Approximately 5 million
Pakistanis2 have migrated to different countries around the world during 1970-2008.
Majority of the Pakistanis have migrated to the Middle East countries such as to Saudi
Arabia (2.3 Milliom) and UAE (1.3 million). Other major concentrations and absorptions
are Oman and Kuwait. Pakistani workers also migrated to several other countries both
developing and developed around the world.
In the decade of 1970s, the total amount of remittances sent home by the migrant workers
increases as the number of migrant to Middle East increases. However remittance flows
continue to decline after reaching a peak in 1982-83. The declining trend in remittances
continues to persist till 2001. The Pakistani overseas migrant showed a recurring and
fluctuating behavior from 1971 to 2008.
2 Figures on overseas Pakistanis are from Bureau of Emigration and Overseas Employment.
Remittances
Investment
Consumptions
Foreign Exchange
Education Health
Foreign Asset
Domestic Asset
Durable
Non Durable
Current Account Deficit
Human Capital
Economic Growth
6
Fig. 1. Number of Overseas Migrant
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Years
Migrants
Source: Bureau of Emigration and Overseas Employment
The high unemployment, massive poverty, expectations for higher earnings abroad may
be reasons for over the time increase in the number of Pakistanis going abroad. Majority
(i-e 3 million) of migrant workers are either unskilled or semi skilled from low income
backgrounds which allowed their families behind to establish small business, get hold of
real assets and make considerable and extensive enhancement and improvement in their
standard of living.
Figure 2 shows the skill composition of migrant workers to different countries around the
world. Out of the total, 1.8% is highly qualified (engineers and doctors), 7% are highly
skilled, 44% are skilled 46.3% are semi skilled and unskilled including masons,
carpenters, technician, followed by electrician, and mechanics.
.
Source: Bureau of Emigration and Overseas Employment
Fig. 2. Skill Composition of workers since 1970-2008
2%7%
2%
44%
45%
Highly Qualified
Highly Skilled
Skilled
Semi Skilled
Unskilled
7
Among the highly qualified, the category demanded most was that of the (1.8%). Over
the time the process of human capital flight or brain drain is imposing monetary cost to
the economy as highly qualified and skilled workers also take with them the skills and
trainings supported and sponsored by the government.
Overseas worker's Employment: Category Wise39%
7%
4%0.8%
5%3%
3%
4%
34%
Engineer/Doctors
Agriculturist
Labourers
Mason
Carpenter
Electrician
Mechanic
Technician
Others
Source: Bureau of Emigration and Overseas Employment
Furthermore a large proportion of migrated workers are laborers (38.8 percent) and
agriculturalist (3.7 percent) which can have substantial and considerable effect on
domestic labor supply in case both categories of workers continue to migrate.
Geographical distribution of origin of workers is shown in Figure 3. as can be seen most
of the workers belong to Punjab (48.8%) followed by NWFP (24.6%), Sindh (8.7%) and
Balochistan (6.4%). The relatively low overseas migration from Sindh points to the fact
that better job opportunities and business environment may possibly be prevailing there
which encourages and persuade people to stay in home country.
8
Fig. 2. Migrated Workers: Province Wise
25%
9%
49%
6% 6%
0.08% 5%
Punjab
Sind
NWFP
Baluchistan
Azad Kashmir
Niarea
Tribal Area
Source: Bureau of Emigration and Overseas Employment
Overseas Migration and Poverty District wise analysis of overseas migration and poverty3 for the provinces of Punjab
(Figure 5), Sind (Figure 6) and Balochistan (Figure 8) confirms that percentage of people
below the poverty line are lower in those districts where the percentage of migrated
workers is higher. Additionally 48.8 percent of overseas migrant belongs to the province
of Punjab which points to the fact that remittance inflows from abroad may be the reason
for reducing poverty in the districts of Punjab. However the district wise analysis of
NWFP is mixed as the Figure 7 is not portraying a clear picture and pointing to the fact
that there may possibly be some other factors that are contributing to the occurrence of
poverty.
Table 1: Correlations
Poverty
Overseas Migrant
Poverty Pearson Correlation
1 -.369**
Sig. (2-tailed) .000 Overseas Migrants
Pearson Correlation
-.369** 1
Sig. (2-tailed) .000 **. Correlation is significant at the 0.01 level (2-tailed).
3 Data on district wise poverty has been taken from Jamal (2007), Income poverty at District Level: An Application of Small Area Estimation Technique
9
Furthermore, correlation between overseas migrants and the percentage of population
below the poverty line also demonstrates that overseas migration is making its
contribution in achieving the objective of poverty alleviation and improving the standard
of living of citizens.
Fig: 5. Overseas Migration and poverty: District Wise Analysis of Punjab
11 12 12 1314 14
17 18 18 19 19 19 2022
24 2426 26
2830 30
32 32
3537
38 39 3941
4648
51
5456
5.97.3
5.03.5
0.42.8
0.31.6 2.7
1.0 1.5 1.60.1 0.9 1.4 1.0 0.6 0.6 0.9 0.6 0.7 1.6
0.21.6 0.7 0.9 0.4 0.8 0.5
3.00.8 0.7
2.2
5.1
0
10
20
30
40
50
60
Raw
alpindi
Lahore
Jhelum
Gujrat
Sialkot
Attock
Mandi Bhauddin
Chakw
al
Bhakkar
T.T.Singh
Gujranw
ala
Narow
al
Faisalabad
Sahiwal
Hafiza Ab
ad
Khushab
Sargodha
Sheikhupura
Kasur
Okara
Vehari
Jhang
Bahawalnagar
Mianw
ali
Pakpattan
Multan
Khanew
al
Bahawalpur
Leiah
R. Y
. Khan
Lodhran
D.G.Khan
Rajanpur
Muzaffargarh
(%)
% of population below the poverty line(04-05) % of migrants_district wise
Fig. 6. Overseas Migration and poverty: District Wise Anaysis of Sindh
9
2325 25
29 29
33 33 34 3536
43
47
51
1.90.5 0.2 0.9 0.1 0.2 0.5 0.1 0.2 0.2 0.6 0.4 0.1 0.2
0
10
20
30
40
50
60
Karach
i
Hyderab
ad
Sang
har
Sukk
ur
Mirpur K
has
Tharpa
rkar
Nawab
Sha
h
Noshe
ro Feroz
Jaco
baba
d
Badin
Dadu
Larkan
a
Thatt
a
Shika
rpur
(%)
% of population below the poverty line(04-05) % of migrants_district wise
10
Fig. 7. Overseas Migration and Poverty: District Wise Analysis of NWFP
21 21
27 27 28 29 29
3335 35 35 36 37 37
39 4041 41
42
45 46
51
2.75.3
2.1 2.4 1.4
9.9
0.1
5.9
0.02.4
0.6 1.13.4
2.2
5.37.1
1.3 1.64.1
2.30.3 0.1
0
10
20
30
40
50
60
Manse
hra
Abotab
ad
Haripu
r
Swabi
Nowsh
era
Kohat
Batagra
m
Bannu
Lower
Dir
D.I.Kha
n Ta
nk
Kohist
an
Pesha
war Kark
Malaka
nd
Swat
Charsa
da
Chitral
Mardan
Bonair
Lakk
i Marw
at
Shang
la
(%)
% of population below the poverty line(04-05) % of migrants_district wise
Fig. 8. Overseas Migration and Poverty: District Wise Analysis of Balochistan
34
42
4850 51 52 53 54 56 56 57 59
6162
66 66
77
1.3 0.8 0.3 1.0 0.7 0.2 0.3 0.7 0.8 0.2 0.2 0.0 0.1 0.4 1.2 0.4 0.80
10
20
30
40
50
60
70
80
90
Que
tta
Kalat
Gwa
dar
Panjgu
r
Khuzda
r
Loralai
Jhal M
agsi
Ketch/Tu
rbat
Kharan
Sibb
i
Nasirab
ad
Qilla
Abd
ullah
Qilla
h Sa
ifulla
Pash
in
Zhob
Lasbela
Chag
hi
(%)
% of population below the poverty line(04-05) % of migrants_district wise
In case of Pakistan recorded remittances sent home by migrant from abroad has reached $
6000 millions in 2007 from $1100 millions in 2000. The exact magnitude of remittances
is believed to be even lager involving both recorded and unrecorded flows through formal
11
and informal channels. Table A in appendix shows that in decade of 1980s and after 9/11
incident remittances inflow were quite high. During 1970s average annual inflow of
remittances were 4.2 percentages of GDP and during 1980s average annual inflow of
remittances reached at 7.5 percent of GDP. In the decade of 1990s remittances inflow
declined at its lowest level and reached to 2.9 percent of GDP. After 2000 again
remittances inflow start rising and reached to 4.2 percent of GDP in 2007.
Figure: 8 Trends in GDP and Remittances
0
2
4
6
8
10
12
14
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
LGDP LREM
Figure 8 depicts the positive relationship between GDP and remittances. GDP and
remittances inflow show an increasing trend till 1983, however after 1983 remittances
inflow depict consistently declining trend till 2000 and GDP increase at decreasing rate
during this period. After 2000 both series move in the upward direction. The figure
makes it clear that over the time, trend in remittances and GDP growth are almost the
same.
Figure 9 exhibits relationship between poverty and remittances for Pakistan during 1973-
2006. Right vertical axis represents the remittances as percentage of GDP and left vertical
axis poverty (head count) estimate. The figure shows that poverty is negatively related to
increase in remittances during the period of 1973-2006 in Pakistan. In the decade of
1980s poverty decreased coupled with increase in remittance inflows whereas decrease in
remittance inflow during 1990s is associated with increase in poverty with remittances
reaching its lowest point. However after the incident of 9/11 remittances inflow sharply
moves in upward direction with poverty starting to decline as well.
12
Figure: 9 Poverty and Remittances Trend in Pakistan 1973 to 2006
1 5
2 0
2 5
3 0
3 5
4 0
4 5
0
2
4
6
8
1 0
1 2
7 5 8 0 8 5 9 0 9 5 0 0 0 5
P O V R E M
The above data analysis is also supported by the empirical finding in literature as
Siddiqui and Kemal (2006) pointed out that increase in poverty during 1990s may be due
to short fall of remittances in this decade. Therefore data and literature both support the
hypothesis that remittance contributes to poverty reduction in Pakistan.
4) Methodology
Theoretical as well as empirical literature predicts that remittances contribute not only in
the growth process of recipient country but also play an important role in reducing
poverty. This study intends to explore the effect of remittances on real GDP and poverty
in Pakistan, we specify following two independent models to deal with remittances,
growth and poverty.
(i) Remittances and Growth
We specify an empirical to explore the impact of remittances on economic
growth. The Model is as;
tttttOt OPHDIINVREMLRGDP εα +∂+∂+∂+∂+= 4321 (1)
13
WhereRGDP ,REM ,INV, HDI and OP are log of real GDP, remittances as percentage
of GDP, gross fixed capital formation as percentage of GDP, human development index4
,OP is trade openness, respectively. ε is well behaves error term.
Previous studies suggest that remittances effect the economic growth positively through
reducing the current account deficit, external borrowing and availability of foreign
exchange (Iqbal and Sattar, 2005). The impact of human capital, investment and trade
openness on output is assumed to be positive.
(ii) Poverty and Remittances
We used similar to the model suggested by Ravallion (1997), Ravallion and Chen
(1997) and Adam and Page (2005) to explore the impact of remittances on poverty. The
model is written as,
ttttt LREMLIEQLRGDPLP ωα βββ ++++=321
(2)
Where LP is a measure of poverty, LRGDP is real gross domestic product, LIEQ is
income inequality, LREM is remittances and ε is well behaved error term. The expected
signs of β1, β2, and β3 are negative, positive and positive/negative respectively.
To estimate both models in equation (1) and (2), Autoregressive Distributed Lag
(ARDL)5 method developed by Pesaran et al. (2001) has been used. This technique is
more appropriate for small sample size and can be implemented irrespective of whether
the underlying variables are I (0) or I (1). In this approach long run and short run
parameters of the model are estimated simultaneously. ARDL formulation can be written
as follow
εβββββ +∆++++=∆−
=−
=−− ∑∆∑ ZYZY it
k
iit
k
itT
Y1
51
413121 (3)
Where Y is dependent variables, Z is the vector of explanatory variables included in the
regression equation 1 and 2. Bounds testing procedure develop by Pesaran et al (2001) is
used to test the presence of long run relationship among the variable in equation (3). The
test based on F test for cointegration analysis. The null hypothesis is that the
4calculated on the basis of three dimensions of human development, leading a long and healthy life, literacy rate and school enrollment, and having a decent standard of living measured by GDP per capita 5 ARDL methodology is well established in the literature and there is no need to give detailed account here.
14
coefficients β2 and β3 are jointly equal to zero. In other words the null hypothesis states
that there is no long run relationship between the variables in equation (3). The computed
F-statistics is compared with the critical value bounds of the F-statistic. If computed F-
statistic higher than the upper bound of the critical value of F-statistic, the null hypothesis
would be rejected and vice versa.
Annual data from 1973 to 2007 has been used to analyze the effect of remittances on real
output and poverty. Data on gross domestic product (GDP), remittances and gross fixed
capital formation proxy for investment are obtained from World Bank (2008). Data on
human development index (HDI) are taken from United Nations Development Program
(UNDP)6. Data on poverty (.i.e. Headcount ratio) and income inequality are taken from
Jamal (2006) for the period from 1973 to 2003 and extended to 2006 using the same
methodology used by Jamal (2006).
5) Empirical Results
Before estimation the time series property of the data has been examined to determine
their order of integration by using Augmented Dickey Fuller (ADF) unit root test. The
results are reported in table 1
Table: 1 Test of non-stationarity of Variables
Variables Constant/ Trend Level First Difference
LP c, t -3.711*
LREM c, t -1.951 -4.55*
LRGDP c, t -0.927 -4.71*
LIEQ c -5.99*
HDI c, t -5.20*
OP c, t -2.827 -6.53*
Note;* indicate significant at 5% level. c,t denotes constant and trend
6 http://hdr.undp.org/statistics/data/indicators.cfm?x=16&y=1&z=1
15
As can be seen from the table, LRGDP, REM and OP are non-stationary at level and
become stationary after taking first difference. This implies that these three series are
integrated of order one, i.e. I (1) while LP, LIEQ HDI are stationary at level, i.e. I (0).
Table 1 shows that order of integration of all the variables is not same, therefore the
mixed results obtained from the unit root test justify using ARDL technique to estimate
the long-run and short-run relationship among the variables under investigation.
5.1 Remittances and Economic Growth.
Equation (1) is estimated by using lag length of order three which is selected on
the basis of AIC and SBC criteria. The final form of the model selected is subject to all
the diagnostic tests. Results of diagnostic tests are reported in panel B of table 2. Panel A
of table 2 reports the results of the unrestricted error correction modeling.
To determine the cointegration relationship among the real GDP, remittances, investment
and HDI, we test the hypothesis that the coefficients of the lag level variables are equal to
zero. First differences of the variables explain the short run effect of explanatory
variables on the dependent variable. Based on the redundant variable test, we obtained F-
statistics equal to 14.17. This value is higher than that the upper bound of the tabulated
value .i.e. 4.32 (Panel C of table 2).
In order to find the long run coefficients, we normalized remittances; investment, HDI
and openness by real GDP and results are presented in panel (D) of Table 2. It can be
evident from Table 2 that in the long run remittances are positively related to economic
growth. In the short run the effect of remittances on GDP is negative; however the
magnitude of this variable is small and negligible. Investment and human development
index positively and significantly affect GDP in long run as well as in short run. Results
shows that the trade openness is negative but insignificant, negative sign of trade
openness could be due to the reason that trade is more likely to be based on imports of
consumption goods. In panel (D) of table 2 the long run coefficient of investment and
human development index are consistent with economic theory showing their importance
for growth and development.
16
Table: 2 Estimate of Growth and Remittances Equation
Dependent Variable: tLRGDP∆
Note: p- values are stated in [ ]. Breusch-Godfrey Serial Correlation LM and ARCH Test
are based on F-statistics. While normality test is based on Chi-square test
A: Model
Variable Coefficient t-Statistic
1−∆ tRGDP 0.690345 2.986167
2−∆ tLRGDP 0.295166 2.250736
tINV∆ 0.870203 3.569036
1−∆ tREM -0.926541 -4.256735
3−∆ tREM 0.567470 2.621423
tHDI∆ 0.235796 2.006562
3−∆ tOP 0.151114 1.926871
1−tLRGP -2.555010 -7.799704
1−tINV 0.910469 3.824714
1−tREM 1.189132 6.566328
1−tHDI 0.275218 3.299042
1−tOP -0.137629 -1.237194
C -17.40686 -3.057456
Adjusted R-squared 0.81
DW 1.94
B: Diagnostic Tests
Serial Correlation LM Test 1.3743 [0.2813]
ARCH Test 0.0819 [0.7767]
Jarque-Bera(2) 1.1647 [0.5585]
Ramsey RESET Test 2.3709[0.1420]
C Cointegration Test
F-statistics (5,18) 14.177[0.000010]
D: Long Run Coefficients
REM 0.465
INV 0.3563
HDI 0.1077
OP -0.054
C -6.81
17
5.2 Empirical Results of Poverty and Remittances To estimate equation (2), we used general to specific approach on the basis of AIC
and SBC criteria, select lag length of order 3 and remove the insignificant variables from
the model. After all the diagnostic checks (reported in panel B of table 3) we select the
estimated model.
Table: 3 Estimate of Poverty and Remittances Equation
Dependent Variable: tLP∆
Note: p- values are stated in [ ]. Breusch-Godfrey Serial Correlation LM Test, ARCH Test, are based on F-statistics. While normality test is based on Chi-square test
A: Model Variable Coefficient t-Statistic
1−tLP -0.0164 -9.31
1−tLREM -0.0023 -4.03
1−tLIEQ .003 3.84
1−tLRGP -0.01 -2.67
1−∆ tLP 1.068 20.3
2−∆ tLP -0.073 -2.99
tLREM∆ -0.0007 -1.676
1−∆ tLREM 0.001 2.84
tLIEQ∆ --0.524 -36.35
1−∆ tLIEQ 0.646 17.16
1−∆ tLRGDP 0.01 3.519 Adjusted R-squared 0.99 S.E. of regression 0.000427
B: Diagnostic Tests Breusch-Godfrey Serial Correlation LM Test
ARCH Test 0.962029[0.33507]
Jarque-Bera (2) 0.6579 [0.7196]
Ramsey RESET Test 4.081[0.057678]
C: Cointegration Test F-statistics (4,20) 77.57 [0.000]
D: Long- Run Coefficients LREM -0.13795 LIEQ 0.1899
LRGDP -0.61577
18
To find the long run relationship among poverty, remittances, income inequality and real
GDP, test the hypothesis that the coefficients of lag variables are equal to zero based on
the redundant variable test. Results of cointegration test are presented in panel (C) of
table 3. The results suggest that the null hypothesis of no long run relationship is
rejected, because the computed F-statistics is highly significant. This implies that the long
run relationship exist among poverty, remittances, real GDP and income inequality. We
get the long run coefficients by normalizing the level explanatory variables and results
reported in panel (D) of table 3. The results suggest that an increase in remittances can
directly lead to poverty reduction in the long run. This may be due to the fact that
remittances directly increase the income of poor people, smooth household consumption
and ease capital constraint. The short run impact of remittances on poverty is negative
which might be due to the transaction cost associated with migration. The long run
elasticity of poverty with respect to income inequality (Gini coefficient) is positive and
significant which is according to expectation. This positive and significant relation
indicate that at a given rate of economic growth, poverty reduces more in low inequality
countries, as opposed to high inequality countries, so the income inequality variable is
positive and significant (Adam and Page, 2005). Long run poverty elasticity with respect
to real GDP is positive and significant which is consistent with economic theory. The
magnitude of the coefficient of long run variable is consistent with analysis of poverty
reduction (Adam and page, 2005).
6) Conclusion
The study mainly focused on the importance of workers’ remittances inflow and its
implication for economic growth and poverty reduction. By using the ARDL approach
we analyze the impact of remittances inflow on economic growth and poverty. It is found
that remittances effect economic growth positively and significantly. Findings emerge
from this study that remittances have a strong and statistically significant impact on
poverty reduction and growth in Pakistan.
The finding of this study suggests that international migration of labour has substantial
potential benefits for poor people in developing countries like Pakistan. In the long run
19
the remittance inflow can leads to sustainable growth and welfare improvement and
upgradation of poor households as the impact of remittance broaden and enlarge over the
time. So the government should formulate the policy that enhances the amount of
remittances by reducing the transaction cost of transferring the remittances through
formal channel.
20
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23
Appendix
Table -A: GDP Growth and Remittances
Years GDP (%) Remittances
( Millions US $)
Remittances (%of GDP)
1970s 4.8 546.13 4.2
1980s 6.5 962.86 7.5
1990s 4.6 1019.58 2.9
2000 3.9
1075 1.5
2001 2 1461 2.0
2002 4.7 3554 4.9
2003 7.5 3964 4.8
2004 8.6 3945 4.0
2005 6.6 4280 3.9
2006 6.5 5121 4.0
2007 5.8 5998 4.2
24
Stability Test for Growth and Remittances Equation
-0.4
0.0
0.4
0.8
1.2
1.6
1990 1992 1994 1996 1998 2000 2002 2004 2006
CUSUM of Squares 5% Significance
-15
-10
-5
0
5
10
15
1990 1992 1994 1996 1998 2000 2002 2004 2006
CUSUM 5% Significance
Stability test for Poverty and Remittances Equation
-0.4
0.0
0.4
0.8
1.2
1.6
90 92 94 96 98 00 02 04 06
CUSUM of Squares 5% Significance